ANTECEDENTS AND CONSEQUENCES OF TRUST IN SUPPLY CHAIN: THE ROLE OF INFORMATION TECHNOLOGY

Trust has been a central construct in studies of inter-firm relationships. Many operational, organizational, social, and cultural factors have been identified to have significant impact on inter-firm trust. In this study, we investigate the role of information technology in generating inter-firm trust and the consequences of this trust in the context of supply networks. Using structural equation modeling techniques, our data show that the level of information systems integration among the partner firms in a supply network significantly impacts the trust among the firms which, together with the integrated information systems, explains more than half of the variances in information sharing and business process coupling in the network. Given the substantial evidence in the literature on the impact of information sharing and process coupling on supply chain performance, we conclude that information systems integration among the partners is critical to supply network performance. We also confirm that information systems flexibility and use of standards in information systems significantly contribute to the level of systems integration among the partners in supply networks as suggested in prior studies. Our findings extend the current literature on inter-firm trust by considering the role of information technology in addition to other important factors already identified

Geotag propagation in social networks based on user trust model

In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmark

A Detailed Dominant Data Mining Approach for Predictive Modeling of Social Networking Data using WEKA

Social network has gained popularity manifold in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. In this Paper, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. As the popularity increases and they became widely used as one of the important sources of news, people become more cautious about determining the trustworthiness of the information which is disseminating through social media for various reasons. For this reason, knowing the factors that influence the trust in social media content became very important. In this research paper, we use a survey as a mechanism to study trust in social networks. First, we prepared a questionnaire which focuses on measuring the ways in which social network users determine whether content is true or not and then we analyzed the response of individuals who participated in the survey and discuss the results in a focus group session. Then, the responses, we get from the survey and the focus group was used as a dataset for modeling trust, which incorporates factors that alter trust determination. The dataset preprocessing a total of 56 records were used for building the models. This Paper applies the Decision Tree, Bayesian Classifiers and Neural Network predictive data mining techniques in significant social media factors for predicting trust. To accomplish this goal: The WEKA data mining tool is used to evaluate the J48, Na�ve Bayes and Multilayer Perception algorithms with different experiments were made by performing adjustments of the attributes and using various numbers of attributes in order to come up with a purposeful output

A Detailed Dominant Data Mining Approach for Predictive Modeling of Social Networking Data using WEKA

Social network has gained popularity manifold in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. In this Paper, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. As the popularity increases and they became widely used as one of the important sources of news, people become more cautious about determining the trustworthiness of the information which is disseminating through social media for various reasons. For this reason, knowing the factors that influence the trust in social media content became very important. In this research paper, we use a survey as a mechanism to study trust in social networks. First, we prepared a questionnaire which focuses on measuring the ways in which social network users determine whether content is true or not and then we analyzed the response of individuals who participated in the survey and discuss the results in a focus group session. Then, the responses, we get from the survey and the focus group was used as a dataset for modeling trust, which incorporates factors that alter trust determination. The dataset preprocessing a total of 56 records were used for building the models. This Paper applies the Decision Tree, Bayesian Classifiers and Neural Network predictive data mining techniques in significant social media factors for predicting trust. To accomplish this goal: The WEKA data mining tool is used to evaluate the J48, Naïve Bayes and Multilayer Perception algorithms with different experiments were made by performing adjustments of the attributes and using various numbers of attributes in order to come up with a purposeful output

The Impact Of Organizational Goal Convergence, Information-communication Technology Utilization, And Inter-organizational Trust On Network Formation And Sustainability The Case Of Emergency Management In The United States

With the increase of severity and scope of disasters, collaborative networks have become the main tool to tackle with complex emergencies. Networks, however, are mostly effective to the extent they are maintained over time. This study analyzes whether organizational goal convergence, information-communication technology utilization, and inter-organizational trust impacts network sustainability. The main research questions of the study are: (1) How are organizational goals, technical/technological capacity of organizations, and trust among organizations of a network are related to the sustainability of collaborative network relationships? (2) Which of the above-mentioned factors plays the most significant role in affecting network sustainability? Covering the context of emergency management system in the United States, this study utilized a self-administered survey that was electronically distributed to county emergency managers across the country. The data consisting of 534 complete responses was analyzed in Statistical Package for the Social Sciences (SPSS) Inc. software‟s PASW (Predictive Analytics SoftWare) Statistics version 18.0 and transferred to Amos 18.0 software for structural equation modeling (SEM) analysis. The findings suggest that organizational goal convergence, information-communication technology utilization, and inter-organizational trust have positive and statistically significant relationships with network sustainability; and, interorganizational trust is the strongest factor followed by information-communication technology utilization and organizational goal convergence. The study contributes to the literature on network sustainability with specific suggestions for emergency management practitioners

Semantic Modeling for Group Formation

Group formation has always been a subject of interest in collaborative learning research. As it is concerned with assigning learners to the groups that maximize their benefits, computer-supported group formation can be viewed in this context as an active personalization for the individual as an entity within the group. While applying this personalization to all students in the class can cause conflicts due to the differences of needs and interests between the individuals, negotiating the allocations to groups to reach consensus can be a very challenging task. The automated process of grouping students while preserving the individual’s personalization needs to be supported by an appropriate learner model. In this paper, we propose a semantic learner model based on the Friend of Friend (FOAF) ontology, a vocabulary for mapping social networks. We discuss the model as we analyse the different types of groups and the learners’ features that need to be modeled for each of these types

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN